{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Armed Bandits and Reinforcement Learning with Amazon SageMaker\n",
    "\n",
    "We demonstrate how you can manage your own contextual multi-armed bandit workflow on SageMaker using the built-in [AWS Reinforcement Learning Container](https://github.com/aws/sagemaker-rl-container) container to train and deploy contextual bandit models. We show how to train these models that interact with a live environment (using a simulated client application) and continuously update the model with efficient exploration.\n",
    "\n",
    "### Why Contextual Bandits?\n",
    "\n",
    "Wherever we look to personalize content for a user (content layout, ads, search, product recommendations, etc.), contextual bandits come in handy. Traditional personalization methods collect a training dataset, build a model and deploy it for generating recommendations. However, the training algorithm does not inform us on how to collect this dataset, especially in a production system where generating poor recommendations lead to loss of revenue. Contextual bandit algorithms help us collect this data in a strategic manner by trading off between exploiting known information and exploring recommendations which may yield higher benefits. The collected data is used to update the personalization model in an online manner. Therefore, contextual bandits help us train a personalization model while minimizing the impact of poor recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/multi_armed_bandit_maximize_reward.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To implement the exploration-exploitation strategy, we need an iterative training and deployment system that: (1) recommends an action using the contextual bandit model based on user context, (2) captures the implicit feedback over time and (3) continuously trains the model with incremental interaction data. In this notebook, we show how to setup the infrastructure needed for such an iterative learning system. While the example demonstrates a bandits application, these continual learning systems are useful more generally in dynamic scenarios where models need to be continually updated to capture the recent trends in the data (e.g. tracking fraud behaviors based on detection mechanisms or tracking user interests over time). \n",
    "\n",
    "In a typical supervised learning setup, the model is trained with a SageMaker training job and it is hosted behind a SageMaker hosting endpoint. The client application calls the endpoint for inference and receives a response. In bandits, the client application also sends the reward (a score assigned to each recommendation generated by the model) back for subsequent model training. These rewards will be part of the dataset for the subsequent model training. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relevant Links\n",
    "\n",
    "In-Practice\n",
    "* [AWS Blog Post on Contextual Multi-Armed Bandits](https://aws.amazon.com/blogs/machine-learning/power-contextual-bandits-using-continual-learning-with-amazon-sagemaker-rl/)\n",
    "* [Multi-Armed Bandits at StitchFix](https://multithreaded.stitchfix.com/blog/2020/08/05/bandits/)\n",
    "* [Introduction to Contextual Bandits](https://getstream.io/blog/introduction-contextual-bandits/)\n",
    "* [Vowpal Wabbit Contextual Bandit Algorithms](https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Contextual-Bandit-algorithms)\n",
    "\n",
    "Theory\n",
    "* [Learning to Interact](https://hunch.net/~jl/interact.pdf)\n",
    "* [Contextual Bandit Bake-Off](https://arxiv.org/pdf/1802.04064.pdf)\n",
    "* [Doubly-Robust Policy Evaluation and Learning](https://arxiv.org/pdf/1103.4601.pdf)\n",
    "\n",
    "Code\n",
    "* [AWS Open Source Reinforcement Learning Containers](https://github.com/aws/sagemaker-rl-container)\n",
    "* [AWS Open Source Bandit Experiment Manager](./common/sagemaker_rl/orchestrator/workflow/manager)\n",
    "* [Vowpal Wabbit Reinforcement Learning Framework](https://github.com/VowpalWabbit/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AWS Open Source Bandit `ExperimentManager` Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/multi_armed_bandit_traffic_shift.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bandit model is implemented by the open source [**Bandit Experiment Manager**](./common/sagemaker_rl/orchestrator/workflow/manager/) provided with this example.  This This implementation continuously updates a Vowpal Wabbit reinforcement learning model using Amazon SageMaker, DynamoDB, Kinesis, and S3.\n",
    "\n",
    "The client application, a recommender system with a review service in our case, pings the SageMaker hosting endpoint that is serving the bandit model.  The application sends the an `event` with the `context` (ie. user, product, and review text) to the bandit model and receives a recommended action from the bandit model.  In our case, the action is 1 of 2 BERT models that we are testing.  The bandit model stores this event data (given context and recommended action) in S3 using Amazon Kinesis.  _Note:  The context makes this a \"contextual bandit\" and differentiates this implementation from a regular multi-armed bandit._\n",
    "\n",
    "The client application uses the recommended BERT model to classify the review text as star rating 1 through 5 and  compares the predicted star rating to the user-selected star rating.  If the BERT model correctly predicts the star rating of the review text (ie. matches the user-selected star rating), then the bandit model is rewarded with `reward=1`.  If the BERT model incorrectly classifies the star rating of the review text, the bandit model is not rewarded (`reward=0`).\n",
    "\n",
    "The client application stores the rewards data in S3 using Amazon Kinesis.  Periodically (ie. every 100 rewards), we incrementally train an updated bandit model with the latest the reward and event data.  This updated bandit model is evaluated against the current model using a holdout dataset of rewards and events.  If the bandit model accuracy is above a given threshold relative to the existing model, it is automatically deployed in a blue/green manner with no downtime.  SageMaker RL supports offline evaluation by performing counterfactual analysis (CFA).  By default, we apply [**doubly robust (DR) estimation**](https://arxiv.org/pdf/1103.4601.pdf) method. The bandit model tries to minimize the cost (`1 - reward`), so a smaller evaluation score indicates better bandit model performance.\n",
    "\n",
    "Unlike traditional A/B tests, the bandit model will learn the best BERT model (action) for a given context over time and begin to shift traffic to the best model.  Depending on the aggressiveness of the bandit model algorithm selected, the bandit model will continuously explore the under-performing models, but start to favor and exploit the over-performing models.  And unlike A/B tests, multi-armed bandits allow you to add a new action (ie. BERT model) dynamically throughout the life of the experiment.  When the bandit model sees the new BERT model, it will start sending traffic and exploring the accuracy of the new BERT model - alongside the existing BERT models in the experiment.\n",
    "\n",
    "#### Local Mode\n",
    "\n",
    "To facilitate experimentation, we provide a `local_mode` that runs the contextual bandit example using the SageMaker Notebook instance itself instead of the SageMaker training and hosting cluster instances.  The workflow remains the same in `local_mode`, but runs much faster for small datasets.  Hence, it is a useful tool for experimenting and debugging.  However, it will not scale to production use cases with high throughput and large datasets.  In `local_mode`, the training, evaluation, and hosting is done in the local [SageMaker Vowpal Wabbit Docker Container](https://github.com/aws/sagemaker-rl-container)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "import pandas as pd\n",
    "\n",
    "sess   = sagemaker.Session()\n",
    "bucket = sess.default_bucket()\n",
    "role = sagemaker.get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "sm = boto3.Session().client(service_name='sagemaker', region_name=region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import sys\n",
    "import numpy as np\n",
    "import time\n",
    "import sagemaker\n",
    "\n",
    "sys.path.append('common')\n",
    "sys.path.append('common/sagemaker_rl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import rcParams\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuration\n",
    "\n",
    "The configuration for the bandits application can be specified in a `config.yaml` file as can be seen below. It configures the AWS resources needed. The DynamoDB tables are used to store metadata related to experiments, models and data joins. The `private_resource` specifices the SageMaker instance types and counts used for training, evaluation and hosting. The SageMaker container image is used for the bandits application. This config file also contains algorithm and SageMaker-specific setups.  Note that all the data generated and used for the bandits application will be stored in `s3://sagemaker-{REGION}-{AWS_ACCOUNT_ID}/{experiment_id}/`.\n",
    "\n",
    "Please make sure that the `num_arms` parameter in the config is equal to the number of actions in the client application (which is defined in the cell below).\n",
    "\n",
    "The Docker image is defined here:  https://github.com/aws/sagemaker-rl-container/blob/master/vw/docker/8.7.0/Dockerfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[94mresource\u001b[39;49;00m:\n",
      "  \u001b[94mshared_resource\u001b[39;49;00m:\n",
      "    \u001b[94mresources_cf_stack_name\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mBanditsSharedResourceStack\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m# cloud formation stack\u001b[39;49;00m\n",
      "    \u001b[94mexperiment_db\u001b[39;49;00m:\n",
      "      \u001b[94mtable_name\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mBanditsExperimentTable\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m# Dynamo table for status of an experiment\u001b[39;49;00m\n",
      "    \u001b[94mmodel_db\u001b[39;49;00m:\n",
      "      \u001b[94mtable_name\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mBanditsModelTable\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m# Dynamo table for status of all models trained\u001b[39;49;00m\n",
      "    \u001b[94mjoin_db\u001b[39;49;00m:\n",
      "      \u001b[94mtable_name\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mBanditsJoinTable\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m# Dynamo table for status of all joining job for reward ingestion\u001b[39;49;00m\n",
      "    \u001b[94miam_role\u001b[39;49;00m:\n",
      "      \u001b[94mrole_name\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mBanditsIAMRole\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\n",
      "  \u001b[94mprivate_resource\u001b[39;49;00m:\n",
      "    \u001b[94mhosting_fleet\u001b[39;49;00m:\n",
      "      \u001b[94minstance_type\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mml.t2.medium\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\n",
      "      \u001b[94minstance_count\u001b[39;49;00m: 1\n",
      "    \u001b[94mtraining_fleet\u001b[39;49;00m:\n",
      "      \u001b[94minstance_type\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mml.c5.4xlarge\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\n",
      "    \u001b[94mevaluation_fleet\u001b[39;49;00m:\n",
      "      \u001b[94minstance_type\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mml.c5.4xlarge\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\n",
      "\u001b[94mimage\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33m462105765813.dkr.ecr.{AWS_REGION}.amazonaws.com/sagemaker-rl-vw-container:vw-8.7.0-cpu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m# Vowpal Wabbit container\u001b[39;49;00m\n",
      "\u001b[94malgor\u001b[39;49;00m: \u001b[37m# Vowpal Wabbit algorithm parameters\u001b[39;49;00m\n",
      "  \u001b[94malgorithms_parameters\u001b[39;49;00m:\n",
      "    \u001b[94mexploration_policy\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mcover\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m# supports \"egreedy\", \"bag\", \"cover\"\u001b[39;49;00m\n",
      "    \u001b[94mepsilon\u001b[39;49;00m: 0.10 \u001b[37m# percent to explore with egreedy exploration policy\u001b[39;49;00m\n",
      "    \u001b[94mnum_policies\u001b[39;49;00m: 3 \u001b[37m# number of nested policies to create when using bag or cover exploration policy\u001b[39;49;00m\n",
      "    \u001b[94mnum_arms\u001b[39;49;00m: 2\n",
      "    \u001b[94mcfa_type\u001b[39;49;00m: \u001b[33m\"\u001b[39;49;00m\u001b[33mdr\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m# supports \"dr\", \"ips\"\u001b[39;49;00m\n",
      "\u001b[94mlocal_mode\u001b[39;49;00m: true \u001b[37m# use local mode?\u001b[39;49;00m\n",
      "\u001b[94msoft_deployment\u001b[39;49;00m: true \u001b[37m# use the same endpoint with updated model using a blue-green deployment?\u001b[39;49;00m\n",
      " \n"
     ]
    }
   ],
   "source": [
    "!pygmentize 'config.yaml'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_file = 'config.yaml'\n",
    "with open(config_file, 'r') as yaml_file:\n",
    "    config = yaml.load(yaml_file, Loader=yaml.FullLoader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Client Application (Environment)\n",
    "The client application simulates a live environment that uses the bandit model to recommend a BERT model to classify review text submitted by the application user. \n",
    "\n",
    "The logic of reward generation resides in the client application.  We simulate the online learning loop with feedback.  The data consists of 2 actions - 1 for each BERT model under test.  If the bandit model selects the right class, then the model is rewarded with `reward=1`.  Otherwise, the bandit model receives `reward=0`.\n",
    "\n",
    "The workflow of the client application is as follows:\n",
    "- Our client application picks sample review text at random, which is sent to the bandit model (SageMaker endpoint) to recommend an action (BERT model) to classify the review text into star rating 1 through 5.\n",
    "- The bandit model returns an action, an action probability, and an `event_id` for this prediction event.\n",
    "- Since the client application uses the Amazon Customer Reviews Dataset, we know the true star rating for the review text\n",
    "- The client application compares the predicted and true star rating and assigns a reward to the bandit model using Amazon Kinesis, S3, and DynamoDB.  (The `event_id` is used to join the event and reward data.)\n",
    "\n",
    "`event_id` is a unique identifier for each interaction. It is used to join inference data `<state, action, action_probability>` with the reward data. \n",
    "\n",
    "In a later cell of this notebook, we illustrate how the client application interacts with the bandit model endpoint and receives the recommended action (BERT model)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step-by-step bandits model development\n",
    "\n",
    "[**Bandit Experiment Manager**](./common/sagemaker_rl/orchestrator/workflow/manager/) is the top level class for all the Bandits/RL and continual learning workflows. Similar to the estimators in the [Sagemaker Python SDK](https://github.com/aws/sagemaker-python-sdk), `ExperimentManager` contains methods for training, deployment and evaluation. It keeps track of the job status and reflects current progress in the workflow.\n",
    "\n",
    "Start the application using the `ExperimentManager` class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "timestamp = int(time.time())\n",
    "\n",
    "experiment_name = 'bandits-{}'.format(timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `ExperimentManager` will create a AWS CloudFormation Stack of additional resources needed for the Bandit experiment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:orchestrator.resource_manager:Using Resources in CloudFormation stack named: BanditsSharedResourceStack for Shared Resources.\n"
     ]
    }
   ],
   "source": [
    "from orchestrator.workflow.manager.experiment_manager import ExperimentManager\n",
    "\n",
    "bandit_experiment_manager = ExperimentManager(config, experiment_id=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize the Client Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "class ClientApp():\n",
    "    def __init__(self, data, num_events, bandit_model, bert_model_map):\n",
    "        self.bandit_model = bandit_model\n",
    "        self.bert_model_map = bert_model_map\n",
    "        \n",
    "        self.num_actions = 2\n",
    "\n",
    "        df_reviews = pd.read_csv(data, \n",
    "                                 delimiter='\\t', \n",
    "                                 quoting=csv.QUOTE_NONE,\n",
    "                                 compression='gzip')\n",
    "        df_scrubbed = df_reviews[['review_body', 'star_rating']].sample(n=num_events) # .query('star_rating == 1')\n",
    "        df_scrubbed = df_scrubbed.reset_index()\n",
    "        df_scrubbed.shape\n",
    "        np_reviews = df_scrubbed.to_numpy()\n",
    "\n",
    "        np_reviews = np.delete(np_reviews, 0, 1)\n",
    "        \n",
    "        # Last column is the label, the rest are the features (contexts)\n",
    "        self.labels = np_reviews[:, -1]\n",
    "        self.contexts = np_reviews[:, :-1].tolist()\n",
    "\n",
    "        self.optimal_rewards = [1]\n",
    "        self.rewards_tmp_buffer = []\n",
    "        self.joined_data_tmp_buffer = []\n",
    "        self.all_joined_data_buffer = []\n",
    "        \n",
    "        self.action_count = {}\n",
    "\n",
    "    def increment_action_count(self, action):\n",
    "        try:\n",
    "            action_count = self.action_count[action]\n",
    "        except:\n",
    "            self.action_count[action] = 0\n",
    "            action_count = 0\n",
    "            \n",
    "        self.action_count[action] = action_count + 1\n",
    "                \n",
    "    def choose_random_context(self):\n",
    "        context_index = np.random.choice(len(self.contexts))\n",
    "        context = self.contexts[context_index]\n",
    "        return context_index, context    \n",
    "\n",
    "    def clear_tmp_buffers(self):\n",
    "        self.rewards_tmp_buffer.clear()\n",
    "        self.joined_data_tmp_buffer.clear()\n",
    "\n",
    "    def get_reward(self, \n",
    "                   context_index, \n",
    "                   action, \n",
    "                   event_id, \n",
    "                   bandit_model_id, \n",
    "                   action_prob, \n",
    "                   sample_prob, \n",
    "                   local_mode):\n",
    "\n",
    "        context_to_predict = self.contexts[context_index][0]\n",
    "    \n",
    "        label = self.labels[context_index]\n",
    "        \n",
    "        bert_model = self.bert_model_map[action]\n",
    "\n",
    "        self.increment_action_count(action)\n",
    "        \n",
    "        # TensorFlow returns [str]\n",
    "        if (action == 1):\n",
    "            bert_predicted_class = bert_model.predict(context_to_predict)[0]\n",
    "            bert_predicted_class\n",
    "            print('Predicted Class from Model 1: {}, Actual Class: {}'.format(bert_predicted_class, label))\n",
    "\n",
    "        # PyTorch returns bytes\n",
    "        if (action == 2):\n",
    "            bert_predicted_class = bert_model.predict({\"review_body\": context_to_predict})\n",
    "            bert_predicted_class = bert_predicted_class# .decode('utf-8')\n",
    "            print('Predicted Class from Model 2: {}, Actual Class: {}'.format(bert_predicted_class, label))\n",
    "            \n",
    "       # Calculate difference between predicted and actual label\n",
    "        if abs(int(bert_predicted_class) - int(label)) == 0:\n",
    "            reward = 1\n",
    "        else:\n",
    "            reward = 0\n",
    "\n",
    "        if local_mode:\n",
    "            json_blob = {\n",
    "                         \"reward\": reward,\n",
    "                         \"event_id\": event_id,\n",
    "                         \"action\": action,\n",
    "                         \"action_prob\": action_prob,\n",
    "                         \"model_id\": bandit_model_id,\n",
    "                         \"observation\": [context_index],\n",
    "                         \"sample_prob\": sample_prob\n",
    "                        }\n",
    "            \n",
    "            self.joined_data_tmp_buffer.append(json_blob)            \n",
    "        else:\n",
    "            json_blob = {\n",
    "                         \"reward\": reward, \n",
    "                         \"event_id\": event_id\n",
    "                        }\n",
    "            self.rewards_tmp_buffer.append(json_blob)\n",
    "        \n",
    "        return reward\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "bandit_model = bandit_experiment_manager.predictor\n",
    "\n",
    "client_app = ClientApp(data='./data/amazon_reviews_us_Digital_Software_v1_00.tsv.gz',\n",
    "                       num_events=100,\n",
    "                       bandit_model=bandit_model,\n",
    "                       bert_model_map={\n",
    "                         1: model1,\n",
    "                         2: model2\n",
    "                       })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Bandit Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can train a new model with newly collected experiences, and host the resulting model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained bandit model id bandits-1610163172-model-id-1610163261\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:orchestrator:Use last trained model bandits-1610163172-model-id-1610163261 as pre-trained model for training\n",
      "INFO:orchestrator:Starting training job for ModelId 'bandits-1610163172-model-id-1610163337''\n",
      "INFO:orchestrator:Training job will be executed in 'local' mode\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating 7ru878yq03-algo-1-2mrdy ... \n",
      "\u001b[1BAttaching to 7ru878yq03-algo-1-2mrdy2mdone\u001b[0m\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 2021-01-09 03:35:40,003 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 2021-01-09 03:35:40,013 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 2021-01-09 03:35:40,025 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 2021-01-09 03:35:40,033 sagemaker-containers INFO     Invoking user script\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m Training Env:\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"additional_framework_parameters\": {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"sagemaker_estimator\": \"RLEstimator\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     },\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"channel_input_dirs\": {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"training\": \"/opt/ml/input/data/training\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"pretrained_model\": \"/opt/ml/input/data/pretrained_model\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     },\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"current_host\": \"algo-1-2mrdy\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"framework_module\": null,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"hosts\": [\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"algo-1-2mrdy\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     ],\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"hyperparameters\": {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"exploration_policy\": \"cover\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"epsilon\": 0.1,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"num_policies\": 3,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"num_arms\": 2,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"cfa_type\": \"dr\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     },\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"input_data_config\": {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"training\": {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m             \"TrainingInputMode\": \"File\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         },\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"pretrained_model\": {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m             \"TrainingInputMode\": \"File\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m             \"ContentType\": \"application/x-sagemaker-model\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         }\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     },\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"input_dir\": \"/opt/ml/input\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"is_master\": true,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"job_name\": \"bandits-1610163172-model-id-1610163337\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"log_level\": 20,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"master_hostname\": \"algo-1-2mrdy\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"model_dir\": \"/opt/ml/model\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"module_dir\": \"s3://sagemaker-us-east-1-835319576252/bandits-1610163172/training_jobs/bandits-1610163172-model-id-1610163337/source/sourcedir.tar.gz\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"module_name\": \"train-vw\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"network_interface_name\": \"eth0\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"num_cpus\": 8,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"num_gpus\": 0,\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"output_dir\": \"/opt/ml/output\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"resource_config\": {\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"current_host\": \"algo-1-2mrdy\",\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         \"hosts\": [\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m             \"algo-1-2mrdy\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m         ]\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     },\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m     \"user_entry_point\": \"train-vw.py\"\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m }\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m Environment variables:\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_HOSTS=[\"algo-1-2mrdy\"]\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_NETWORK_INTERFACE_NAME=eth0\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_HPS={\"cfa_type\":\"dr\",\"epsilon\":0.1,\"exploration_policy\":\"cover\",\"num_arms\":2,\"num_policies\":3}\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_USER_ENTRY_POINT=train-vw.py\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_FRAMEWORK_PARAMS={\"sagemaker_estimator\":\"RLEstimator\"}\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_RESOURCE_CONFIG={\"current_host\":\"algo-1-2mrdy\",\"hosts\":[\"algo-1-2mrdy\"]}\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_INPUT_DATA_CONFIG={\"pretrained_model\":{\"ContentType\":\"application/x-sagemaker-model\",\"TrainingInputMode\":\"File\"},\"training\":{\"TrainingInputMode\":\"File\"}}\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_OUTPUT_DATA_DIR=/opt/ml/output/data\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_CHANNELS=[\"pretrained_model\",\"training\"]\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_CURRENT_HOST=algo-1-2mrdy\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_MODULE_NAME=train-vw\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_LOG_LEVEL=20\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_FRAMEWORK_MODULE=\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_INPUT_DIR=/opt/ml/input\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_INPUT_CONFIG_DIR=/opt/ml/input/config\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_OUTPUT_DIR=/opt/ml/output\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_NUM_CPUS=8\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_NUM_GPUS=0\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_MODEL_DIR=/opt/ml/model\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_MODULE_DIR=s3://sagemaker-us-east-1-835319576252/bandits-1610163172/training_jobs/bandits-1610163172-model-id-1610163337/source/sourcedir.tar.gz\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_TRAINING_ENV={\"additional_framework_parameters\":{\"sagemaker_estimator\":\"RLEstimator\"},\"channel_input_dirs\":{\"pretrained_model\":\"/opt/ml/input/data/pretrained_model\",\"training\":\"/opt/ml/input/data/training\"},\"current_host\":\"algo-1-2mrdy\",\"framework_module\":null,\"hosts\":[\"algo-1-2mrdy\"],\"hyperparameters\":{\"cfa_type\":\"dr\",\"epsilon\":0.1,\"exploration_policy\":\"cover\",\"num_arms\":2,\"num_policies\":3},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"pretrained_model\":{\"ContentType\":\"application/x-sagemaker-model\",\"TrainingInputMode\":\"File\"},\"training\":{\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"bandits-1610163172-model-id-1610163337\",\"log_level\":20,\"master_hostname\":\"algo-1-2mrdy\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-1-835319576252/bandits-1610163172/training_jobs/bandits-1610163172-model-id-1610163337/source/sourcedir.tar.gz\",\"module_name\":\"train-vw\",\"network_interface_name\":\"eth0\",\"num_cpus\":8,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1-2mrdy\",\"hosts\":[\"algo-1-2mrdy\"]},\"user_entry_point\":\"train-vw.py\"}\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_USER_ARGS=[\"--cfa_type\",\"dr\",\"--epsilon\",\"0.1\",\"--exploration_policy\",\"cover\",\"--num_arms\",\"2\",\"--num_policies\",\"3\"]\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_CHANNEL_TRAINING=/opt/ml/input/data/training\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_CHANNEL_PRETRAINED_MODEL=/opt/ml/input/data/pretrained_model\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_HP_EXPLORATION_POLICY=cover\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_HP_EPSILON=0.1\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_HP_NUM_POLICIES=3\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_HP_NUM_ARMS=2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m SM_HP_CFA_TYPE=dr\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m PYTHONPATH=/opt/ml/code:/usr/local/bin:/vowpal_wabbit/python:/vowpal_wabbit/build/python:/usr/lib/python36.zip:/usr/lib/python3.6:/usr/lib/python3.6/lib-dynload:/usr/local/lib/python3.6/dist-packages:/usr/lib/python3/dist-packages\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m Invoking script with the following command:\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m /usr/bin/python train-vw.py --cfa_type dr --epsilon 0.1 --exploration_policy cover --num_arms 2 --num_policies 3\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:root:channels ['pretrained_model', 'training']\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:root:hps: {'cfa_type': 'dr', 'epsilon': 0.1, 'exploration_policy': 'cover', 'num_arms': 2, 'num_policies': 3}\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:root:Loading model from /opt/ml/input/data/pretrained_model/vw.model\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:VW CLI:creating an instance of VWModel\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:VW CLI:successfully created VWModel\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:VW CLI:command: ['vw', '--cb_explore', '2', '--cover', '3', '-i', '/opt/ml/input/data/pretrained_model/vw.model', '-f', '/opt/ml/model/vw.model', '--save_resume', '-p', '/dev/stdout']\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:VW CLI:Started VW process!\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:root:Processing training data: [PosixPath('/opt/ml/input/data/training/local-joined-data-1610163329.csv')]\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m final_regressor = /opt/ml/model/vw.model\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m predictions = /dev/stdout\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m Num weight bits = 18\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m learning rate = 0.5\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m initial_t = 0\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m power_t = 0.5\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m using no cache\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m Reading datafile = \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m num sources = 1\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m average  since         example        example  current  current  current\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m loss     last          counter         weight    label  predict features\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 0.000000 0.000000            1            1.0        2 2:1.000000        2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 0.000000 0.000000            2            2.0        2 2:1.000000        2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 0.309875 0.619750            4            4.0        2 2:0.591752        2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 0.455562 0.601248            8            8.0        2 2:0.666667        2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 0.570214 0.684866           16           16.0        2 1:0.666667        2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 0.549731 0.529247           32           32.0        2 2:1.000000        2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 0.586826 0.623921           64           64.0        2 1:0.666667        2\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m finished run\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m number of examples = 91\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m weighted example sum = 91.000000\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m weighted label sum = 0.000000\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m average loss = 0.600240\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m total feature number = 182\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m \n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m INFO:root:Model learned using 91 training experiences.\n",
      "\u001b[36m7ru878yq03-algo-1-2mrdy |\u001b[0m 2021-01-09 03:35:40,487 sagemaker-containers INFO     Reporting training SUCCESS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:sagemaker.local.image:Failed to delete: /tmp/tmp9cnsilyp/algo-1-2mrdy Please remove it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m7ru878yq03-algo-1-2mrdy exited with code 0\n",
      "\u001b[0mAborting on container exit...\n",
      "===== Job Complete =====\n"
     ]
    }
   ],
   "source": [
    "bandit_experiment_manager.train_next_model(input_data_s3_prefix=bandit_experiment_manager.last_joined_job_train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy the Bandit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deploying bandit model id bandits-1610163172-model-id-1610163261\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:orchestrator:Model 'bandits-1610163172-model-id-1610163337' is ready to deploy.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Found new model! Trying to replace Model ID: bandits-1610163172-model-id-1610163261 with Model ID: bandits-1610163172-model-id-1610163337\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [25] [INFO] Handling signal: hup\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [25] [INFO] Hang up: Master\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [54] [INFO] Booting worker with pid: 54\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [55] [INFO] Booting worker with pid: 55\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] creating an instance of VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] successfully created VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] command: ['vw', '--cb_explore', '2', '--cover', '3', '-p', '/dev/stdout', '--quiet', '--testonly', '-i', '/opt/ml/downloads/q8HG0p0B/vw.model']\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Loaded weights successfully for Model ID:bandits-1610163172-model-id-1610163337\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [56] [INFO] Booting worker with pid: 56\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [40] [INFO] Worker exiting (pid: 40)\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [41] [INFO] Worker exiting (pid: 41)\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [44] [INFO] Worker exiting (pid: 44)\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [42] [INFO] Worker exiting (pid: 42)\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] creating an instance of VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] successfully created VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] command: ['vw', '--cb_explore', '2', '--cover', '3', '-p', '/dev/stdout', '--quiet', '--testonly', '-i', '/opt/ml/downloads/q8HG0p0B/vw.model']\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Loaded weights successfully for Model ID:bandits-1610163172-model-id-1610163337\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [2021-01-09 03:35:51 +0000] [57] [INFO] Booting worker with pid: 57\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] creating an instance of VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] successfully created VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] command: ['vw', '--cb_explore', '2', '--cover', '3', '-p', '/dev/stdout', '--quiet', '--testonly', '-i', '/opt/ml/downloads/q8HG0p0B/vw.model']\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Loaded weights successfully for Model ID:bandits-1610163172-model-id-1610163337\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Started VW process!\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] creating an instance of VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] successfully created VWModel\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] command: ['vw', '--cb_explore', '2', '--cover', '3', '-p', '/dev/stdout', '--quiet', '--testonly', '-i', '/opt/ml/downloads/q8HG0p0B/vw.model']\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Loaded weights successfully for Model ID:bandits-1610163172-model-id-1610163337\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Started VW process!\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Started VW process!\n",
      "\u001b[36mtfwd76283e-algo-1-h18li |\u001b[0m [01/09/2021 03:35:51 INFO 140070204577536] Started VW process!\n"
     ]
    }
   ],
   "source": [
    "print('Deploying bandit model id {}'.format(bandit_experiment_manager.last_trained_model_id))\n",
    "\n",
    "bandit_experiment_manager.deploy_model(model_id=bandit_experiment_manager.last_trained_model_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Experiment Status:  DEPLOYED\n",
    "`deploying_state`:  `SUCCEEDED`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'experiment_id': 'bandits-1610163172',\n",
       " 'training_workflow_metadata': {'next_model_to_train_id': None,\n",
       "  'last_trained_model_id': 'bandits-1610163172-model-id-1610163337',\n",
       "  'training_state': 'TRAINED'},\n",
       " 'hosting_workflow_metadata': {'hosting_endpoint': 'local:arn-does-not-matter',\n",
       "  'hosting_state': <HostingState.DEPLOYED: 'DEPLOYED'>,\n",
       "  'last_hosted_model_id': 'bandits-1610163172-model-id-1610163337',\n",
       "  'next_model_to_host_id': None},\n",
       " 'joining_workflow_metadata': {'joining_state': 'SUCCEEDED',\n",
       "  'last_joined_job_id': 'bandits-1610163172-join-job-id-1610163328',\n",
       "  'next_join_job_id': None},\n",
       " 'evaluation_workflow_metadata': {'evaluation_state': None,\n",
       "  'last_evaluation_job_id': None,\n",
       "  'next_evaluation_job_id': None}}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bandit_experiment_manager._jsonify()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continuously Train, Evaluate, and Deploy Bandit Models\n",
    "The above cells explained the individual steps in the training workflow. To train a model to convergence, we will continually train the model based on data collected with client application interactions. We demonstrate the continual training and evaluation loop in a single cell below.\n",
    "\n",
    "_**Train and Evaluate**_:\n",
    "After every training cycle, we evaluate if the newly trained model (`last_trained_model_id`) would perform better than the one currently deployed (`last_hosted_model_id`) using a holdout evaluation dataset.  Details of the join, train, and evaluation steps are tracked in the `BanditsJoinTable` and `BanditsModelTable` DynamoDB tables.  When you have multiple experiments, you can compare them in the `BanditsExperimentTable` DynamoDB table.\n",
    "\n",
    "_**Deploy**_: If the new bandit model is better than the current bandit model (based on offline evaluation), we will automatically deploy the new bandit model using a blue-green deployment to avoid downtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "do_evaluation = True\n",
    "total_loops = 5 # Increase for higher accuracy\n",
    "retrain_batch_size = 100 # Model will be trained after every `batch_size` number of data instances\n",
    "rewards_list = []\n",
    "event_list = []\n",
    "\n",
    "all_joined_train_data_s3_uri_list = []\n",
    "all_joined_eval_data_s3_uri_list = []\n",
    "\n",
    "local_mode = bandit_experiment_manager.local_mode\n",
    "\n",
    "start_time = time.time()\n",
    "for loop_no in range(total_loops):\n",
    "    print(f\"\"\"\n",
    "    ################################\n",
    "    # Incremental Training Loop {loop_no+1}\n",
    "    ################################\n",
    "    \"\"\")\n",
    "    \n",
    "    # Generate experiences and log them\n",
    "    for i in range(retrain_batch_size):\n",
    "        context_index, context = client_app.choose_random_context()\n",
    "        action, event_id, bandit_model_id, action_prob, sample_prob = bandit_model.get_action(obs=[context_index])\n",
    "\n",
    "        reward = client_app.get_reward(context_index=context_index, \n",
    "                                       action=action, \n",
    "                                       event_id=event_id, \n",
    "                                       bandit_model_id=bandit_model_id, \n",
    "                                       action_prob=action_prob, \n",
    "                                       sample_prob=sample_prob, \n",
    "                                       local_mode=local_mode)\n",
    "\n",
    "        rewards_list.append(reward)\n",
    "        \n",
    "    # Publish rewards sum for this batch to CloudWatch for monitoring \n",
    "    bandit_experiment_manager.cw_logger.publish_rewards_for_simulation(\n",
    "        bandit_experiment_manager.experiment_id,\n",
    "        sum(rewards_list[-retrain_batch_size:])/retrain_batch_size\n",
    "    )\n",
    "    \n",
    "    # Join the events and rewards data to use for the next bandit-model training job\n",
    "    # Use 90% as the training dataset and 10% as the the holdout evaluation dataset\n",
    "    if local_mode:        \n",
    "        bandit_experiment_manager.ingest_joined_data(client_app.joined_data_tmp_buffer,\n",
    "                                                     ratio=0.90)\n",
    "    else:\n",
    "        # Kinesis Firehose => S3 => Athena\n",
    "        print('Waiting for firehose to flush data to s3...')\n",
    "        time.sleep(60) \n",
    "        rewards_s3_prefix = bandit_experiment_manager.ingest_rewards(client_app.rewards_tmp_buffer)\n",
    "        bandit_experiment_manager.join(rewards_s3_prefix, ratio=0.90)\n",
    "            \n",
    "    # Train \n",
    "    bandit_experiment_manager.train_next_model(\n",
    "        input_data_s3_prefix=bandit_experiment_manager.last_joined_job_train_data)\n",
    "\n",
    "    all_joined_train_data_s3_uri_list.append(bandit_experiment_manager.last_joined_job_train_data)\n",
    "\n",
    "    # Evaluate and/or deploy the new bandit model\n",
    "    if do_evaluation:\n",
    "        bandit_experiment_manager.evaluate_model(\n",
    "            input_data_s3_prefix=bandit_experiment_manager.last_joined_job_eval_data,\n",
    "            evaluate_model_id=bandit_experiment_manager.last_trained_model_id)\n",
    "\n",
    "        eval_score_last_trained_model = bandit_experiment_manager.get_eval_score(\n",
    "            evaluate_model_id=bandit_experiment_manager.last_trained_model_id,\n",
    "            eval_data_path=bandit_experiment_manager.last_joined_job_eval_data)\n",
    "\n",
    "        bandit_experiment_manager.evaluate_model(\n",
    "            input_data_s3_prefix=bandit_experiment_manager.last_joined_job_eval_data,\n",
    "            evaluate_model_id=bandit_experiment_manager.last_hosted_model_id) \n",
    "\n",
    "        all_joined_eval_data_s3_uri_list.append(bandit_experiment_manager.last_joined_job_eval_data)\n",
    "    \n",
    "        # Eval score is a measure of `regret`, so a lower eval score is better\n",
    "        eval_score_last_hosted_model = bandit_experiment_manager.get_eval_score(\n",
    "            evaluate_model_id=bandit_experiment_manager.last_hosted_model_id, \n",
    "            eval_data_path=bandit_experiment_manager.last_joined_job_eval_data)\n",
    "    \n",
    "        print('New bandit model evaluation score {}'.format(eval_score_last_hosted_model))\n",
    "        print('Current bandit model evaluation score {}'.format(eval_score_last_trained_model))\n",
    "\n",
    "        if eval_score_last_trained_model <= eval_score_last_hosted_model:\n",
    "            print('Deploying new bandit model id {} in loop {}'.format(bandit_experiment_manager.last_trained_model_id, loop_no))\n",
    "            bandit_experiment_manager.deploy_model(model_id=bandit_experiment_manager.last_trained_model_id)\n",
    "        else:\n",
    "            print('Not deploying bandit model id {} in loop {}'.format(bandit_experiment_manager.last_trained_model_id, loop_no))\n",
    "    else:\n",
    "        # Just deploy the new bandit model without evaluating against previous model\n",
    "        print('Deploying new bandit model id {} in loop {}'.format(bandit_experiment_manager.last_trained_model_id, loop_no))\n",
    "        bandit_experiment_manager.deploy_model(model_id=bandit_experiment_manager.last_trained_model_id)\n",
    "    \n",
    "    client_app.clear_tmp_buffers()\n",
    "    \n",
    "print(f'Total time taken to complete {total_loops} loops: {time.time() - start_time}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Review Invocations of BERT Model 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Invocations of BERT Model 1:  322\n",
      "Total Invocations of BERT Model 2:  278\n"
     ]
    }
   ],
   "source": [
    "print('Total Invocations of BERT Model 1:  {}'.format(client_app.action_count[1]))\n",
    "print('Total Invocations of BERT Model 2:  {}'.format(client_app.action_count[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "import boto3\n",
    "import pandas as pd\n",
    "\n",
    "cw = boto3.Session().client(service_name='cloudwatch', region_name=region)\n",
    "\n",
    "def get_invocation_metrics_for_endpoint_variant(endpoint_name,\n",
    "                                                namespace_name,\n",
    "                                                metric_name,\n",
    "                                                variant_name,\n",
    "                                                start_time,\n",
    "                                                end_time):\n",
    "    metrics = cw.get_metric_statistics(\n",
    "        Namespace=namespace_name,\n",
    "        MetricName=metric_name,\n",
    "        StartTime=start_time,\n",
    "        EndTime=end_time,\n",
    "        Period=60,\n",
    "        Statistics=[\"Sum\"],\n",
    "        Dimensions=[\n",
    "            {\n",
    "                \"Name\": \"EndpointName\",\n",
    "                \"Value\": endpoint_name\n",
    "            },\n",
    "            {\n",
    "                \"Name\": \"VariantName\",\n",
    "                \"Value\": variant_name\n",
    "            }\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    if metrics['Datapoints']:\n",
    "        return pd.DataFrame(metrics[\"Datapoints\"])\\\n",
    "                .sort_values(\"Timestamp\")\\\n",
    "                .set_index(\"Timestamp\")\\\n",
    "                .drop(\"Unit\", axis=1)\\\n",
    "                .rename(columns={\"Sum\": variant_name})\n",
    "    else:\n",
    "        return pd.DataFrame()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gather BERT Model 1 Invocations Metrics\n",
    "_Please be patient.  This will take 1-2 minutes._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "time.sleep(75)\n",
    "\n",
    "start_time = start_time or datetime.now() - timedelta(minutes=60)\n",
    "end_time = datetime.now()\n",
    "        \n",
    "model_1_endpoint_invocations = get_invocation_metrics_for_endpoint_variant(\n",
    "                                    endpoint_name=model_1_endpoint_name,\n",
    "                                    namespace_name='AWS/SageMaker',                                   \n",
    "                                    metric_name='Invocations',\n",
    "                                    variant_name='AllTraffic',\n",
    "                                    start_time=start_time, \n",
    "                                    end_time=end_time)\n",
    "\n",
    "model_1_endpoint_invocations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gather BERT Model 2 Invocations Metrics\n",
    "_Please be patient.  This will take 1-2 minutes._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "time.sleep(75)\n",
    "\n",
    "start_time = start_time or datetime.now() - timedelta(minutes=60)\n",
    "end_time = datetime.now()\n",
    "        \n",
    "model_2_endpoint_invocations = get_invocation_metrics_for_endpoint_variant(\n",
    "                                    endpoint_name=model_2_endpoint_name,\n",
    "                                    namespace_name='AWS/SageMaker',                                   \n",
    "                                    metric_name='Invocations',\n",
    "                                    variant_name='AllTraffic',\n",
    "                                    start_time=start_time, \n",
    "                                    end_time=end_time)\n",
    "\n",
    "model_2_endpoint_invocations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Invocations')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 588,
       "width": 891
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 15, 10\n",
    "\n",
    "x1 = range(0, model_1_endpoint_invocations.size)\n",
    "y1 = model_1_endpoint_invocations['AllTraffic']\n",
    "plt.plot(x1, y1, label=\"BERT Model 1\")\n",
    "\n",
    "x1 = range(0, model_2_endpoint_invocations.size)\n",
    "y1 = model_2_endpoint_invocations['AllTraffic']\n",
    "plt.plot(x1, y1, label=\"BERT Model 2\")\n",
    "\n",
    "plt.legend(loc=0, prop={'size': 20})\n",
    "plt.xlabel('Time (Minutes)')\n",
    "plt.ylabel('Number of Invocations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check the Invocation Metrics for the BERT Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Review <a target=\"blank\" href=\"https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#metricsV2:namespace=AWS/SageMaker;dimensions=EndpointName,VariantName;search=tensorflow-training-2021-01-06-21-36-03-293-tf-1610159437\">Model 1 SageMaker REST Endpoint</a></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "    \n",
    "display(HTML('<b>Review <a target=\"blank\" href=\"https://console.aws.amazon.com/cloudwatch/home?region={}#metricsV2:namespace=AWS/SageMaker;dimensions=EndpointName,VariantName;search={}\">Model 1 SageMaker REST Endpoint</a></b>'.format(region, model_1_endpoint_name)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Review <a target=\"blank\" href=\"https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#metricsV2:namespace=AWS/SageMaker;dimensions=EndpointName,VariantName;search=tensorflow-training-2021-01-06-21-36-03-293-pt-1610161725\">Model 2 SageMaker REST Endpoint</a></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "\n",
    "display(HTML('<b>Review <a target=\"blank\" href=\"https://console.aws.amazon.com/cloudwatch/home?region={}#metricsV2:namespace=AWS/SageMaker;dimensions=EndpointName,VariantName;search={}\">Model 2 SageMaker REST Endpoint</a></b>'.format(region, model_2_endpoint_name)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Bandit Action Probabilities\n",
    "This is the probability that the bandit model will choose a particular BERT model (action)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Action Probability')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 588,
       "width": 887
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 15, 10\n",
    "\n",
    "x1 = all_joined_data_df.query('action==1').index\n",
    "y1 = all_joined_data_df.query('action==1').action_prob\n",
    "plt.scatter(x1, y1, label=\"BERT Model 1\")\n",
    "\n",
    "x2 = all_joined_data_df.query('action==2').index\n",
    "y2 = all_joined_data_df.query('action==2').action_prob\n",
    "plt.scatter(x2, y2, label=\"BERT Model 2\")\n",
    "\n",
    "plt.legend(loc=3, prop={'size': 20})\n",
    "plt.xlabel('Bandit Model Training Instances')\n",
    "plt.ylabel('Action Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean action probability for BERT Model 1: 0.7435039472049679\n"
     ]
    }
   ],
   "source": [
    "print('Mean action probability for BERT Model 1: {}'.format(all_joined_data_df.query('action==1')['action_prob'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean action probability for BERT Model 2: 0.6956618776978412\n"
     ]
    }
   ],
   "source": [
    "print('Mean action probability for BERT Model 2: {}'.format(all_joined_data_df.query('action==2')['action_prob'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Bandit Sample Probabilities\n",
    "Despite the action probability, we sample from all actions (BERT models).  Below is the sample probability for the chosen BERT model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sample Probability')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 588,
       "width": 887
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 15, 10\n",
    "\n",
    "x1 = all_joined_data_df.query('action==1').index\n",
    "y1 = all_joined_data_df.query('action==1').sample_prob\n",
    "plt.scatter(x1, y1, label=\"BERT Model 1\")\n",
    "\n",
    "x2 = all_joined_data_df.query('action==2').index\n",
    "y2 = all_joined_data_df.query('action==2').sample_prob\n",
    "plt.scatter(x2, y2, label=\"BERT Model 2\")\n",
    "\n",
    "plt.legend(loc=0, prop={'size': 20})\n",
    "plt.xlabel('Bandit Model Training Instances')\n",
    "plt.ylabel('Sample Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean sample probability for BERT Model 1: 0.4991086853034962\n"
     ]
    }
   ],
   "source": [
    "print('Mean sample probability for BERT Model 1: {}'.format(all_joined_data_df.query('action==1')['sample_prob'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean sample probability for BERT Model 2: 0.47657281971124227\n"
     ]
    }
   ],
   "source": [
    "print('Mean sample probability for BERT Model 2: {}'.format(all_joined_data_df.query('action==2')['sample_prob'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Bandit Rewards"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can visualize the bandit-model training performance by plotting the rolling mean reward across client interactions.\n",
    "\n",
    "Here rolling mean reward is calculated on the last `rolling_window` number of data instances, where each data instance corresponds to a single client interaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_window = 100\n",
    "\n",
    "rcParams['figure.figsize'] = 15, 10\n",
    "lwd = 5\n",
    "cmap = plt.get_cmap('tab20')\n",
    "colors=plt.cm.tab20(np.linspace(0, 1, 20))\n",
    "\n",
    "rewards_df = pd.DataFrame(rewards_list, columns=['bandit']).rolling(rolling_window).mean()\n",
    "#rewards_df['perfect'] = sum(client_app.optimal_rewards) / len(client_app.optimal_rewards)\n",
    "\n",
    "rewards_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 609,
       "width": 915
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rewards_df.plot(y=['bandit'],  #, 'perfect'], \n",
    "                linewidth=lwd)\n",
    "plt.legend(loc=4, prop={'size': 20})\n",
    "plt.tick_params(axis='both', which='major', labelsize=15)\n",
    "plt.yticks([0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00])\n",
    "plt.xticks([100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])\n",
    "\n",
    "plt.xlabel('Training Instances (Model is Updated Every %s Instances)' % retrain_batch_size, size=20)\n",
    "plt.ylabel('Rolling {} Mean Reward'.format(rolling_window), size=30)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4724688279301748"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rewards_df['bandit'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monitor the Bandit Model in CloudWatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "You can monitor your Training/Hosting evaluation metrics on this [CloudWatch Dashboard](https://us-east-1.console.aws.amazon.com/cloudwatch/home?region=us-east-1#dashboards:name=bandits-1610163172;start=PT1H)\n",
       "\n",
       "(Note: This would need Trained/Hosted Models to be evaluated in order to publish Evaluation Scores)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from markdown_helper import *\n",
    "from IPython.display import Markdown\n",
    "\n",
    "display(Markdown(bandit_experiment_manager.get_cloudwatch_dashboard_details()))"
   ]
  }
 ],
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   "types_to_exclude": [
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